A Method for the Runtime Validation of AI-based Environment Perception in Automated Driving System
- URL: http://arxiv.org/abs/2412.16762v1
- Date: Sat, 21 Dec 2024 20:21:49 GMT
- Title: A Method for the Runtime Validation of AI-based Environment Perception in Automated Driving System
- Authors: Iqra Aslam, Abhishek Buragohain, Daniel Bamal, Adina Aniculaesei, Meng Zhang, Andreas Rausch,
- Abstract summary: Environment perception is a fundamental part of the dynamic driving task executed by Autonomous Driving Systems.
Current safety-relevant standards for automotive systems assume the existence of comprehensive requirements specifications.
This paper presents a function monitor for the functional runtime monitoring of a two-folded AI-based environment perception for ADS.
- Score: 2.369782235753731
- License:
- Abstract: Environment perception is a fundamental part of the dynamic driving task executed by Autonomous Driving Systems (ADS). Artificial Intelligence (AI)-based approaches have prevailed over classical techniques for realizing the environment perception. Current safety-relevant standards for automotive systems, International Organization for Standardization (ISO) 26262 and ISO 21448, assume the existence of comprehensive requirements specifications. These specifications serve as the basis on which the functionality of an automotive system can be rigorously tested and checked for compliance with safety regulations. However, AI-based perception systems do not have complete requirements specification. Instead, large datasets are used to train AI-based perception systems. This paper presents a function monitor for the functional runtime monitoring of a two-folded AI-based environment perception for ADS, based respectively on camera and LiDAR sensors. To evaluate the applicability of the function monitor, we conduct a qualitative scenario-based evaluation in a controlled laboratory environment using a model car. The evaluation results then are discussed to provide insights into the monitor's performance and its suitability for real-world applications.
Related papers
- An Ontology-based Approach Towards Traceable Behavior Specifications in Automated Driving [0.0]
We propose a Semantic Norm Behavior Analysis as an approach to specify the behavior for an Automated Driving System equipped vehicle.
We use to formally represent specified behavior for a targeted operational environment, and to establish traceability between specified behavior and the stakeholder needs.
Our evaluation shows that the explicit documentation of assumptions in the behavior specification supports both the identification of specification insufficiencies and their treatment.
arXiv Detail & Related papers (2024-09-10T16:00:22Z) - Empowering Autonomous Driving with Large Language Models: A Safety Perspective [82.90376711290808]
This paper explores the integration of Large Language Models (LLMs) into Autonomous Driving systems.
LLMs are intelligent decision-makers in behavioral planning, augmented with a safety verifier shield for contextual safety learning.
We present two key studies in a simulated environment: an adaptive LLM-conditioned Model Predictive Control (MPC) and an LLM-enabled interactive behavior planning scheme with a state machine.
arXiv Detail & Related papers (2023-11-28T03:13:09Z) - Automatic Generation of Scenarios for System-level Simulation-based
Verification of Autonomous Driving Systems [0.0]
This paper describes the framework for system-level simulation-based V&V of autonomous systems using AI components.
The framework is based on a simulation model of the system, an abstract model that describes symbolically the system behavior.
Various coverage criteria can be defined to guide the automated generation of the scenarios.
arXiv Detail & Related papers (2023-11-16T11:03:13Z) - DARTH: Holistic Test-time Adaptation for Multiple Object Tracking [87.72019733473562]
Multiple object tracking (MOT) is a fundamental component of perception systems for autonomous driving.
Despite the urge of safety in driving systems, no solution to the MOT adaptation problem to domain shift in test-time conditions has ever been proposed.
We introduce DARTH, a holistic test-time adaptation framework for MOT.
arXiv Detail & Related papers (2023-10-03T10:10:42Z) - Simulation-based Safety Assurance for an AVP System incorporating
Learning-Enabled Components [0.6526824510982802]
Testing, verification and validation AD/ADAS safety-critical applications remain as one the main challenges.
We explain the simulation-based development platform that is designed to verify and validate safety-critical learning-enabled systems.
arXiv Detail & Related papers (2023-09-28T09:00:31Z) - A Requirements-Driven Platform for Validating Field Operations of Small
Uncrewed Aerial Vehicles [48.67061953896227]
DroneReqValidator (DRV) allows sUAS developers to define the operating context, configure multi-sUAS mission requirements, specify safety properties, and deploy their own custom sUAS applications in a high-fidelity 3D environment.
The DRV Monitoring system collects runtime data from sUAS and the environment, analyzes compliance with safety properties, and captures violations.
arXiv Detail & Related papers (2023-07-01T02:03:49Z) - Towards Audit Requirements for AI-based Systems in Mobility Applications [0.0]
We propose 50 technical requirements or best practices that extend existing regulations and address the concrete needs for deep neural networks (DNNs)
We show the applicability, usefulness and meaningfulness of the proposed requirements by performing an exemplary audit of a DNN-based traffic sign recognition system.
arXiv Detail & Related papers (2023-02-27T07:57:52Z) - Differentiable Control Barrier Functions for Vision-based End-to-End
Autonomous Driving [100.57791628642624]
We introduce a safety guaranteed learning framework for vision-based end-to-end autonomous driving.
We design a learning system equipped with differentiable control barrier functions (dCBFs) that is trained end-to-end by gradient descent.
arXiv Detail & Related papers (2022-03-04T16:14:33Z) - Robustness Enhancement of Object Detection in Advanced Driver Assistance
Systems (ADAS) [0.0]
The proposed system includes two main components: (1) a compact one-stage object detector which is expected to be able to perform at a comparable accuracy compared to state-of-the-art object detectors, and (2) an environmental condition detector that helps to send a warning signal to the cloud in case the self-driving car needs human actions due to the significance of the situation.
arXiv Detail & Related papers (2021-05-04T15:42:43Z) - Cycle and Semantic Consistent Adversarial Domain Adaptation for Reducing
Simulation-to-Real Domain Shift in LiDAR Bird's Eye View [110.83289076967895]
We present a BEV domain adaptation method based on CycleGAN that uses prior semantic classification in order to preserve the information of small objects of interest during the domain adaptation process.
The quality of the generated BEVs has been evaluated using a state-of-the-art 3D object detection framework at KITTI 3D Object Detection Benchmark.
arXiv Detail & Related papers (2021-04-22T12:47:37Z) - Towards robust sensing for Autonomous Vehicles: An adversarial
perspective [82.83630604517249]
It is of primary importance that the resulting decisions are robust to perturbations.
Adversarial perturbations are purposefully crafted alterations of the environment or of the sensory measurements.
A careful evaluation of the vulnerabilities of their sensing system(s) is necessary in order to build and deploy safer systems.
arXiv Detail & Related papers (2020-07-14T05:25:15Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.